A Novel Contrast Enhancement Algorithm for Infrared Laser Images

A conventional piecewise linear grey transformation based self-adaptive contrast enhancement algorithm is proposed for infrared laser images, in which the image pixel value histogram is divided into three parts: background and noise area, targets area, and uninterested area. The targets parts are highlighted, while the background and noise parts and the uninterested parts are restrained. A comprehensive qualitative and quantitative image enhancement performance evaluation is presented to verify the proposed theory and algorithm validity, efficiency and reasonability. The experimental results indicate that the proposed algorithm can greatly improve the global and local contrast for both near infrared images and far infrared laser images while efficiently reducing noise in the infrared laser images, and the visual quality of enhanced image is obviously better than the enhancement of the traditional histogram equalization, double plateaus histogram equalization algorithm, etc.